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整合单细胞和批量RNA测序分析鉴定出与调节性细胞死亡相关的基因特征,用于预测皮肤黑色素瘤的预后和治疗反应。

Integrated Single-Cell and Bulk RNA Sequencing Analysis Identifies a Regulated Cell Death-Related Gene Signature for Predicting Prognosis and Therapeutic Responses in Cutaneous Melanoma.

作者信息

Tang Jie, Zhu Shixing, Zhu Linyu

机构信息

Department of Dermatology, Dazhu People's Hospital, Dazhou, Sichuan, China.

Department of Dermatology, Dazhou Central Hospital, Dazhou, Sichuan, China.

出版信息

J Immunother. 2025 Oct 1;48(8):285-299. doi: 10.1097/CJI.0000000000000564.

DOI:10.1097/CJI.0000000000000564
PMID:40444735
Abstract

Regulated cell death (RCD) is crucial for the advancement of cancers, and providing opportunities as prognostic indicators and immunotherapy markers for patients with cutaneous melanoma (CM). Ten multiomics integrative clustering approaches were performed to identify the CM subtypes. Subsequently, WGCNA was used to screen for module genes. Furthermore, screening hub genes was conducted through machine-learning analyses. Two CM subclasses were identified based on RCD multiomics profiling, each exhibiting distinctive molecular patterns. Then, utilizing the shared cluster DEGs, prognosis DEGs, and key module genes, 30 hub genes were obtained, and an RCD.score was conducted based on these genes. The RCD.score not only reflected the characteristics of the clinical but also provided insights into immunotherapy efficacy. Specifically, low RCD.score category exhibited a more active TME and favorable prognosis, those in the low RCD.score category was more responsive to immunotherapy, suggesting an inflamed TME phenotype. The high RCD.score category had a poor prognosis and was lower responsive to immunotherapy. This research offers genetic support for the possible therapeutic advantages of focusing on RCD in the treatment of CM while also examining their underlying pathophysiological mechanisms.

摘要

程序性细胞死亡(RCD)对癌症进展至关重要,并为皮肤黑色素瘤(CM)患者提供了作为预后指标和免疫治疗标志物的机会。采用了十种多组学整合聚类方法来识别CM亚型。随后,使用加权基因共表达网络分析(WGCNA)筛选模块基因。此外,通过机器学习分析进行枢纽基因筛选。基于RCD多组学分析确定了两个CM亚类,每个亚类都表现出独特的分子模式。然后,利用共享聚类差异表达基因(DEGs)、预后DEGs和关键模块基因,获得了30个枢纽基因,并基于这些基因计算了RCD评分。RCD评分不仅反映了临床特征,还为免疫治疗疗效提供了见解。具体而言,低RCD评分类别表现出更活跃的肿瘤微环境(TME)和良好的预后,低RCD评分类别的患者对免疫治疗更敏感,表明TME呈炎症表型。高RCD评分类别预后较差,对免疫治疗的反应较低。本研究为在CM治疗中关注RCD的潜在治疗优势提供了遗传学支持,同时也研究了其潜在的病理生理机制。

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